Unsupervised deep-learning approach for time domain signal denoising in MPI

Denoising is crucial in Magnetic Particle Imaging (MPI) since the measured signals are inherently affected by noise and distortions that degrade the reconstruction quality. This work explores an unsupervised deep-learning approach for time-domain denoising of real-world MPI data, which is based on a Wasserstein Generative Adversarial Network (WGAN). The results show that unsupervised deep learning-based denoising can enhance the signal quality of 1D MPI data by increasing the sensitivity and reducing reconstruction artifacts.